Senior Data Scientist in Mossville, Illinois

Senior Data Scientist

Your work shapes the world at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do - but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here - we make it happen, with our customers, where we work and live.

Together, we are building a better world, so we can all enjoy living in it.

The Strategic Procurement and Planning Division (SPPD) is seeking a Senior Data Scientist to join the AI Mission Team, partnering with enterprise AI Accelerator teams and business leaders to deliver next-generation AI-enabled solutions. This role is responsible for transforming high-priority supply chain, procurement, and planning challenges into scalable AI prototypes, agentic workflows, and production-ready solution patterns. You will design and implement solutions leveraging machine learning, large language models (LLMs), retrieval-augmented generation (RAG), and workflow automation, ensuring alignment with enterprise architecture, governance, and security standards.

This position blends deep technical expertise with strong business partnership to accelerate digital transformation and operational efficiency.

The ideal candidate brings hands-on experience in AI application development, Python, APIs, data integration, and responsible AI practices, along with the ability to prototype rapidly and scale solutions globally.

• Design, prototype, build, test, and deploy AI-enabled applications and internal tools using Python, Streamlit, APIs, and modern AI frameworks
• Deliver scalable AI/ML solutions that solve complex business problems across procurement, planning, and supply chain
• Develop agent-based workflows, tool integrations, and human-in-the-loop processes
• Automate operational decision-making workflows to drive efficiency and scalability
• Implement RAG, GraphRAG, embeddings, vector databases, and knowledge graph solutions
• Enable AI applications to retrieve and reason over trusted enterprise data sources
• Apply AI governance practices including model evaluation, prompt/version tracking, PII masking, guardrails, and cost monitoring
• Ensure compliance with enterprise data security, privacy, and risk management standards
• Design and automate pipelines for structured and unstructured data from enterprise and legacy systems
• Support scalable data ingestion, transformation, and integration frameworks
• Partner with product owners, SMEs, and digital teams to translate business problems into AI/ML prototypes and scalable solutions
• Define success criteria, KPIs, and measurable outcomes for AI initiatives

• Programming
  
  Languages:
  
  Knowledge of programming concepts and software development practices; ability to write, test, and maintain code in Python and integrate with APIs, databases, and enterprise platforms. Builds and deploys AI-enabled applications, automation workflows, and agent-based solutions, integrating large language models (LLMs), vector databases, and enterprise systems while ensuring secure data handling and scalable architecture.
• Requirements Analysis:
  Knowledge of tools, methods, and techniques for eliciting, analyzing, and documenting business and technical requirements for successful solution delivery. Translates business problems into AI/ML use cases, prototypes, and scalable solution patterns, defining measurable success criteria and aligning outputs to enterprise strategy and operational outcomes.
• Analytical Thinking:
  Knowledge of techniques and tools that promote effective analysis; ability to determine root cause, evaluate alternatives, and recommend practical solutions. Applies structured problem-solving to design and evaluate AI-driven approaches (e.g., automation, LLM-based solutions, agentic workflows) and determine the most effective and scalable path forward.
• Business Statistics:
  Knowledge of statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical methods to support business decisions. Supports model validation, experimentation, and performance evaluation, ensuring AI/ML outputs are reliable, measurable, and aligned to business KPIs.
• Machine Learning:
  Knowledge of machine learning principles, algorithms, and evaluation methods; ability to develop, implement, and improve AI/ML-enabled solutions. Designs and delivers solutions using advanced machine learning, large language models (LLMs), retrieval-augmented generation (RAG), embeddings, and model evaluation techniques, incorporating responsible AI practices such as bias awareness, model monitoring, guardrails, and performance optimization.

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related quantitative field
• Advanced degrees preferred with focus on AI, machine learning, or applied data…